Collision-free Autonomous Navigation of A Small UAV Using Low-cost Sensors in GPS-denied Environments

This paper proposes a novel complete navigation system for autonomous flight of small unmanned aerial vehicles (UAVs) in GPS-denied environments. The hardware platform used to test the proposed algorithm is a small, custom-built UAV platform equipped with an onboard computer, RGB-D camera, 2D light detection and ranging (LiDAR), and altimeter. The error-state Kalman filter (ESKF) based on the dynamic model for low-cost IMU-driven systems is proposed, and visual odometry from the RGB-D camera and height measurement from the altimeter are fed into the measurement update process of the ESKF. The pose output of the ESKF is then integrated into the open-source simultaneous location and mapping (SLAM) algorithm for pose-graph optimization and loop closing. In addition, the computationally efficient collision-free path planning algorithm is proposed and verified through simulations. The software modules run onboard in real time with limited onboard computational capability. The indoor flight experiment demonstrates that the proposed system for small UAVs with low-cost devices can navigate without collision in fully autonomous missions while establishing accurate surrounding maps.

     

Bearing-only visual SLAM for small unmanned aerial vehicles in GPS-denied environments

This paper presents a hierarchical simultaneous localization and mapping(SLAM) system for a small unmanned aerial vehicle(UAV) using the output of an inertial measurement unit(IMU) and the bearing-only observations from an onboard monocular camera.A homography based approach is used to calculate the motion of the vehicle in 6 degrees of freedom by image feature match.This visual measurement is fused with the inertial outputs by an indirect extended Kalman filter(EKF) for attitude and velocity estimation.Then,another EKF is employed to estimate the position of the vehicle and the locations of the features in the map.Both simulations and experiments are carried out to test the performance of the proposed system.The result of the comparison with the referential global positioning system/inertial navigation system(GPS/INS) navigation indicates that the proposed SLAM can provide reliable and stable state estimation for small UAVs in GPS-denied environments.     

SOFT‐SLAM: Computationally efficient stereo visual simultaneous localization and mapping for autonomous unmanned aerial vehicles

Autonomous navigation of unmanned aerial vehicles (UAVs) in GPS‐denied environments is a challenging problem, especially for small‐scale UAVs characterized by a small payload and limited battery autonomy. A possible solution to the aforementioned problem is vision‐based simultaneous localization and mapping (SLAM), since cameras, due to their dimensions, low weight, availability, and large information bandwidth, circumvent all the constraints of UAVs. In this paper, we propose a stereo vision SLAM yielding very accurate localization and a dense map of the environment developed with the aim to compete in the European Robotics Challenges (EuRoC) targeting airborne inspection of industrial facilities with small‐scale UAVs. The proposed approach consists of a novel stereo odometry algorithm relying on feature tracking (SOFT), which currently ranks first among all stereo methods on the KITTI dataset. Relying on SOFT for pose estimation, we build a feature‐based pose graph SLAM solution, which we dub SOFT‐SLAM. SOFT‐SLAM has a completely separate odometry and mapping threads supporting large loop‐closing and global consistency. It also achieves a constant‐time execution rate of 20 Hz with deterministic results using only two threads of an onboard computer used in the challenge. The UAV running our SLAM algorithm obtained the highest localization score in the EuRoC Challenge 3, Stage IIa–Benchmarking, Task 2. Furthermore, we also present an exhaustive evaluation of SOFT‐SLAM on two popular public datasets, and we compare it to other state‐of‐the‐art approaches, namely ORB‐SLAM2 and LSD‐SLAM. The results show that SOFT‐SLAM obtains better localization accuracy on the majority of datasets sequences, while also having a lower runtime.     

Dual FastSLAM: Dual Factorization of the Particle Filter Based Solution of the Simultaneous Localization and Mapping Problem

The process of building a map with a mobile robot is known as the Simultaneous Localization and Mapping (SLAM) problem, and is considered essential for achieving true autonomy. The best existing solutions to the SLAM problem are based on probabilistic techniques, mainly derived from the basic Bayes Filter. A recent approach is the use of Rao-Blackwellized particle filters. The FastSLAM solution factorizes the Bayes SLAM posterior using a particle filter to estimate over the possible paths of the robot and several independent Kalman Filters attached to each particle to estimate the location of landmarks conditioned to the robot path. Although there are several successful implementations of this idea, there is a lack of applications to indoor environments where the most common feature is the line segment corresponding to straight walls. This paper presents a novel factorization, which is the dual of the existing FastSLAM one, that decouples the SLAM into a map estimation and a localization problem, using a particle filter to estimate over maps and a Kalman Filter attached to each particle to estimate the robot pose conditioned to the given map. We have implemented and tested this approach, analyzing and comparing our solution with the FastSLAM one, and successfully building feature based maps of indoor environments.     

面向室内场景的主被动融合视觉定位系统

目的 视觉定位旨在利用易于获取的RGB图像对运动物体进行目标定位及姿态估计。室内场景中普遍存在的物体遮挡、弱纹理区域等干扰极易造成目标关键点的错误估计,严重影响了视觉定位的精度。针对这一问题,本文提出一种主被动融合的室内定位系统,结合固定视角和移动视角的方案优势,实现室内场景中运动目标的精准定位。方法 提出一种基于平面先验的物体位姿估计方法,在关键点检测的单目定位框架基础上,使用平面约束进行3自由度姿态优化,提升固定视角下室内平面中运动目标的定位稳定性。基于无损卡尔曼滤波算法设计了一套数据融合定位系统,将从固定视角得到的被动式定位结果与从移动视角得到的主动式定位结果进行融合,提升了运动目标的位姿估计结果的可靠性。结果 本文提出的主被动融合室内视觉定位系统在iGibson仿真数据集上的平均定位精度为2～3 cm,定位误差在10 cm内的准确率为99%;在真实场景中平均定位精度为3～4 cm,定位误差在10 cm内的准确率在90%以上,实现了cm级的定位精度。结论 提出的室内视觉定位系统融合了被动式和主动式定位方法的优势,能够以较低设备成本实现室内场景中高精度的目标定位结果,并在遮挡、目标...     

基于人工路标的易部署室内机器人全局定位系统

在一些布局易变或存在较多动态障碍物的室内,移动机器人的全局定位依然面临较大的应用挑战.针对这类场景,实现了一种新的基于人工路标的易部署室内机器人全局定位系统.该系统将人工路标粘贴在不易被遮挡的天花板上来作为参照物,仅依赖一个摄像头即能实现稳定的全局定位.整个系统根据具体的功能分为地图构建和全局定位两个过程.在地图构建过程中,系统使用激光SLAM算法所输出的位姿估计结果为基准,根据相机对路标点的观测信息来自动估计人工路标点在全局坐标系中的位姿,建立人工路标地图.而在全局定位过程中,该系统则是根据相机对地图中已知位姿的人工路标点的观测信息,结合里程计与IMU融合的预积分信息来对位姿进行实时估计.充分的实验测试表明,机器人在该系统所部署范围内运行的定位误差稳定在10 cm以内,且运行过程可以保证实时位姿输出,满足典型实际室内移动机器人全局定位的应用需求.     

Indoor UAV Control Using Multi-Camera Visual Feedback

This paper presents the control of an indoor unmanned aerial vehicle (UAV) using multi-camera visual feedback. For the autonomous flight of the indoor UAV, instead of using onboard sensor information, visual feedback concept is employed by the development of an indoor flight test-bed. The indoor test-bed consists of four major components: the multi-camera system, ground computer, onboard color marker set, and quad-rotor UAV. Since the onboard markers are attached to the pre-defined location, position and attitude of the UAV can be estimated by marker detection algorithm and triangulation method. Additionally, this study introduces a filter algorithm to obtain the full 6-degree of freedom (DOF) pose estimation including velocities and angular rates. The filter algorithm also enhances the performance of the vision system by making up for the weakness of low cost cameras such as poor resolution and large noise. Moreover, for the pose estimation of multiple vehicles, data association algorithm using the geometric relation between cameras is proposed in this paper. The control system is designed based on the classical proportional-integral-derivative (PID) control, which uses the position, velocity and attitude from the vision system and the angular rate from the rate gyro sensor. This paper concludes with both ground and flight test results illustrating the performance and properties of the proposed indoor flight test-bed and the control system using the multi-camera visual feedback.     

Localization methods for a mobile robot in urban environments

This paper addresses the problems of building a functional mobile robot for urban site navigation and modeling with focus on keeping track of the robot location. We have developed a localization system that employs two methods. The first method uses odometry, a compass and tilt sensor, and a global positioning sensor. An extended Kalman filter integrates the sensor data and keeps track of the uncertainty associated with it. The second method is based on camera pose estimation. It is used when the uncertainty from the first method becomes very large. The pose estimation is done by matching linear features in the image with a simple and compact environmental model. We have demonstrated the functionality of the robot and the localization methods with real-world experiments.     

室内移动机器人的视觉定位方法研究

针对地图未知的室内环境下的定位问题,提出了一种基于特征跟踪的视觉里程计方法．利用单目摄像头提取和跟踪环境特征点集,进而根据观测模型利用扩展卡尔曼滤波算法估算出机器人的位姿．办公室环境中的定位实验证明了方法的有效性．     

An RGB-D Camera Based Visual Positioning System for Assistive Navigation by a Robotic Navigation Aid

There are about 253 million people with visual impairment worldwide. Many of them use a white cane and/or a guide dog as the mobility tool for daily travel. Despite decades of efforts, electronic navigation aid that can replace white cane is still research in progress. In this paper, we propose an RGB-D camera based visual positioning system (VPS) for real-time localization of a robotic navigation aid (RNA) in an architectural floor plan for assistive navigation. The core of the system is the combination of a new 6-DOF depth-enhanced visual-inertial odometry (DVIO) method and a particle filter localization (PFL) method. DVIO estimates RNA’s pose by using the data from an RGB-D camera and an inertial measurement unit (IMU). It extracts the floor plane from the camera’s depth data and tightly couples the floor plane, the visual features (with and without depth data), and the IMU’s inertial data in a graph optimization framework to estimate the device’s 6-DOF pose. Due to the use of the floor plane and depth data from the RGB-D camera, DVIO has a better pose estimation accuracy than the conventional VIO method. To reduce the accumulated pose error of DVIO for navigation in a large indoor space, we developed the PFL method to locate RNA in the floor plan. PFL leverages geometric information of the architectural CAD drawing of an indoor space to further reduce the error of the DVIO-estimated pose. Based on VPS, an assistive navigation system is developed for the RNA prototype to assist a visually impaired person in navigating a large indoor space. Experimental results demonstrate that: 1) DVIO method achieves better pose estimation accuracy than the state-of-the-art VIO method and performs real-time pose estimation (18 Hz pose update rate) on a UP Board computer; 2) PFL reduces the DVIO-accrued pose error by 82.5% on average and allows for accurate wayfinding (endpoint position error ≤ 45 cm) in large indoor spaces.      

用于无人机室内导航的光流与地标融合方法

针对小型无人机在无卫星导航信号条件下的导航问题,结合光流及地标定位设计了使用摄像头、惯性测量器件、超声测距仪等传感器融合的无人机室内导航方法.文章使用补偿角速率的光流微分法计算帧间像素点小位移,并用前后误差算法提取精度较高的点,避免像素点跟踪错误,提高了光流测速的精度;对得到的光流场用均值漂移算法进行寻优,得到光流场直方图峰值,以此计算光流速度.本文提出了无累积误差的连续地标定位算法,实时测量无人机位置.通过多速率卡尔曼滤波器对观测周期不一致的位置、速度信息进行最优估计.在搭建的八旋翼无人机平台上试验,将位置与速度测量结果分别与激光和PX4FLOW数据对比,结果表明该导航方法可以有效抑制定位跳变与光流测量噪声误差,给出精确的位置与速度估计.     

Vision-aided Estimation of Attitude,Velocity, and Inertial Measurement Bias for UAV Stabilization

This paper studies vision-aided inertial navigation of small-scale unmanned aerial vehicles (UAVs) in GPS-denied environments. The objectives of the navigation system are to firstly online estimate and compensate the unknown inertial measurement biases, secondly provide drift-free velocity and attitude estimates which are crucial for UAV stabilization control, and thirdly give relatively accurate position estimation such that the UAV is able to perform at least a short-term navigation when the GPS signal is not available. For the vision system, we do not presume maps or landmarks of the environment. The vision system should be able to work robustly even given low-resolution images (e.g., 160 ×120 pixels) of near homogeneous visual features. To achieve these objectives, we propose a novel homography-based vision-aided navigation system that adopts four common sensors: a low-cost inertial measurement unit, a downward-looking monocular camera, a barometer, and a compass. The measurements of the sensors are fused by an extended Kalman filter. Based on both analytical and numerical observability analyses of the navigation system, we theoretically verify that the proposed navigation system is able to achieve the navigation objectives. We also show comprehensive simulation and real flight experimental results to verify the effectiveness and robustness of the proposed navigation system.     

基于多智能体强化学习的无人机群室内辅助救援

本文主要研究了在室内场景中使用多台无人机设备对受害者进行合作搜索的问题.在室内场景中,依赖全球定位系统获取受害者位置信息可能是不可靠的.为此,本文提出一种基于多智能体强化学习(MARL)方案,该方案着重对无人机团队辅助救援时的路径规划问题进行研究.相比于传统方案,所提方案在大型室内救援场景中更具优势,例如部署多台救援无...     

一种基于卡尔曼滤波的超宽带室内定位算法

为了减小室内环境中障碍物对超宽带(UWB)传感器测距结果的影响,提出了一种基于卡尔曼滤波(KF)的超宽带室内定位算法.利用超宽带接收信号的信噪比区分视距和非视距环境,给出了超宽带传感器测距性能最小二乘标定模型,减小测距系统误差;判断相邻测距差分是否在阈值范围内,否则用卡尔曼滤波先验估计替代后验估计处理测距结果,由此减弱多径效应和非视距误差对测距的影响;用扩展卡尔曼滤波器(EKF)实现室内定位.实验结果表明:算法在复杂室内环境中可达到亚米级的动态实时定位精度.     

Attitude Determination of Multirotor Aerial Vehicles Using Camera Vector Measurements

The employment of embedded cameras in navigation and guidance of Unmanned Aerial Vehicles (UAV) has attracted the focus of many academic researches. In particular, for the multirotor UAV, the camera is widely employed for applications performed in indoor environments, where the GNSS signal is often unreliable and electromagnetic interference can be a concern. In the literature, images are mostly adopted for position and velocity estimation, rather than attitude estimation. This paper proposes an attitude determination method for multirotor aerial vehicles using pairs of vector measurements taken from a downward-facing strapdown camera. The method is composed of three modules. The first one detects and identifies the visible landmarks by processing the images. The second module computes the vector measurements related to the direction from the camera to the landmarks. The third module estimates attitude from the vector measurements. In the last module, a version of the Multiplicative Extended Kalman Filter (MEKF) with sequential update is proposed as estimation method. The overall method is evaluated via Monte Carlo simulations, showing that it is effective in determining the vehicle’s attitude and revealing its properties.     

Robust mapping and localization in indoor environments

Conventional localization methods have been developed for indoor static environments such as the home environment. In dynamic environments such as factories and warehouses, however, it is difficult to estimate the accurate robot pose. Therefore, we propose a novel approach for the estimation of the robot pose in a dynamic or large environment for which fixed features are used. In the proposed method, a ceiling-feature map is built using an upward-looking monocular camera. This map is created accurately from the robot pose using a laser scanner and an estimation based on the iterative closest point method. The ceiling-feature map consists of features such as lamps and the FREAK, and its creation can be more accurate if the sliding-window technique and bundle-adjustment schemes are used. During the post-mapping navigation, the robot pose is estimated using the Monte Carlo localization method based on the ceiling-feature map. In dynamic experiments, the proposed method shows a high repeatability and stability in real-world conditions and applications.     

Vision-Based Odometry and SLAM for Medium and High Altitude Flying UAVs

This paper proposes vision-based techniques for localizing an unmanned aerial vehicle (UAV) by means of an on-board camera. Only natural landmarks provided by a feature tracking algorithm will be considered, without the help of visual beacons or landmarks with known positions. First, it is described a monocular visual odometer which could be used as a backup system when the accuracy of GPS is reduced to critical levels. Homography-based techniques are used to compute the UAV relative translation and rotation by means of the images gathered by an onboard camera. The analysis of the problem takes into account the stochastic nature of the estimation and practical implementation issues. The visual odometer is then integrated into a simultaneous localization and mapping (SLAM) scheme in order to reduce the impact of cumulative errors in odometry-based position estimation approaches. Novel prediction and landmark initialization for SLAM in UAVs are presented. The paper is supported by an extensive experimental work where the proposed algorithms have been tested and validated using real UAVs.     

Cooperative Control of Multiple UAVs for Moving Source Seeking

Advances in multi-agent technologies and UAV technologies make it possible to take advantage of cooperation of multiple UAVs for source seeking. This paper focuses on moving source seeking using multiple UAVs with input constraints. Firstly, a least-squares method is introduced to estimate the gradient of the scalar field at the leader UAV location based on the measurements of all UAVs. Since the moving source velocity is unknown, an adaptive estimator is designed to obtain the velocity. Based on the estimated gradient and source velocity, a guidance law and a sliding mode based heading rate controller are proposed for the leader UAV to achieve level tracking. Heading rate controller for each follower UAV is also developed to achieve circular formation around the leader UAV. Furthermore, the gradient estimation error is analyzed and its influence on moving source velocity estimation and level tracking accuracy is explored as well. Finally, simulation results are provided to verify the proposed approach.     

基于IMM-IKF的无线传感器网络非视距节点定位方法

针对非视距(NLOS)环境下的移动定位问题,提出一种基于交互式多模型-改进卡尔曼滤波(IMM-IKF)的无线传感器网络NLOS节点定位算法.算法在IMM算法框架下估计移动节点位置,采用两个平行的改进卡尔曼滤波算法对多个移动节点候选位置进行滤波处理,通过Markov链实现LOS状态与NLOS状态的转换,根据似然概率对两个滤波结果进行加权融合,从而获得移动目标位置.仿真实验结果表明,所提出算法可以有效抑制NLOS误差,实现精确定位.     

基于快速不变卡尔曼滤波的视觉惯性里程计

针对相机定位问题,设计基于深度相机和惯性传感器的视觉惯性里程计,里程计包含定位部分和重定位部分.定位部分使用不变卡尔曼滤波融合多层迭代最近点(ICP)的估计值和惯性传感器的测量值来获得精确的相机位姿,其中ICP的估计误差使用费舍尔信息矩阵进行量化.由于需要使用海量的点云作为输入,采用GPU并行计算以快速实现ICP估计和误差量化的过程. 当视觉惯性里程计出现定位失败时,结合惯性传感器数据建立恒速模型,并基于此模型改进随机蕨定位方法,实现视觉惯性里程计的重定位.实验结果表明,所设计的视觉惯性里程计可以获得准确追踪相机且可以进行有效的重定位.